# Fast gpuArray slicing for cart2sph

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Tim on 14 Jul 2020
Edited: Tim on 15 Jul 2020
I have a 3 x N gpuArray, where N is very large, dimension 1 represents x, y, and z, and these points are the result of a viewpoint transformation applied via multiplication of a 3 x N matrix with a 3x3 rotation matrix and sum with a translation vector. At this point I need to convert to spherical coordinates, however to do this I have to slice the array into its x, y, and z components before passing these separate arrays as inputs to cart2sph (or sub functions like atan2 if I want to write my own version).
The problem is that the slicing of the array to feed into cart2sph takes much longer than the viewpoint transformation and the coordinate transform combined. The only way I've found to accelerate this is to replace the slicing with a dot-multiply-and-sum operation, which for some reason is faster than simply slicing. Here's some example code:
tst = randn(3, 6000000, 'single', 'gpuArray');
tic
for n = 1:100
tst1 = tst(1, :);
tst2 = tst(2, :);
tst3 = tst(3, :);
[th, phi, r] = cart2sph(tst1, tst2, tst3);
end
wait(gpuDevice);
toc
tic
for n = 1:100
tst1b = sum(tst.*[1;0;0]);
tst2b = sum(tst.*[0;1;0]);
tst3b = sum(tst.*[0;0;1]);
[th, phi, r] = cart2sph(tst1b, tst2b, tst3b);
end
wait(gpuDevice);
toc
On my computer the first loop takes ~1.5 seconds and the second ~0.5. A dot-multiply & sum is 3x faster than simply slicing the array, and the cart2sph takes only a trivial amount of time. So my questions are:
1) Is there a faster way to get from a 3xN xyz array to a 3xN phi-theta-r array, preferably that does not require a set of (very slow) slicing operations?
2) Why is a multiply and sum operation faster than a simple slicing operation?
Thank you

Edric Ellis on 15 Jul 2020
Like all arrays in MATLAB, gpuArray data is stored in "column-major" order. One consequence of this is that it is much more efficient to extract individual columns from a matrix than individual rows - this is true on the CPU, and doubly true on the GPU. Extracting a column is equivalent to a simple memory block copy. Extracting a row requires a "strided" copy operation. You can take advantage of this by performing a single up-front transpose on your array, and then use efficient column indexing operations:
tst_t = tst.';
for n = 1:100
tst1 = tst_t(:, 1).';
tst2 = tst_t(:, 2).';
tst3 = tst_t(:, 3).';
[th, phi, r] = cart2sph(tst1, tst2, tst3);
end
On my machine with a now rather old Tesla K20c, this approach takes 0.015 seconds compared with 5.3 seconds for your first approach and 2.0 seconds for your second approach. (Note that transposing vectors can be extremely effficient because the memory layout is identical)
Tim on 15 Jul 2020
Brilliant, Edric, was hoping you would weigh in. That makes sense and works on my system, although for some reason my card (RTX 2070) isn't quite as fast as yours for the accelerated version (~0.02 seconds), even though it is ~3x faster for the first two versions. The actual problem I am working on uses much larger 3 x N x M arrays which are created inside the loop, so to get this to work I have to bring the transpose inside the loop and include several reshapes, adding some overhead (predominantly the transpose, it seems). Even so, it is still many times faster than my original versions. Many thanks,